Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	44,838
2	South Dakota	39,613
3	Louisiana	38,963
4	Mississippi	37,673
5	Alabama	35,828
6	Florida	35,503
7	Iowa	35,005
8	Tennessee	33,986
9	Arkansas	33,665
10	Wisconsin	33,014
11	South Carolina	32,310
12	Arizona	32,141
13	Idaho	31,292
14	Nebraska	31,176
15	Georgia	31,044
16	Utah	30,574
17	Texas	30,550
18	Nevada	29,931
19	Illinois	28,422
20	Oklahoma	28,015
21	Rhode Island	27,491
22	Missouri	27,048
23	Kansas	26,189
24	New York	25,354
25	New Jersey	25,332
26	Delaware	24,004
27	North Carolina	23,932
28	District of Columbia	23,376
29	Montana	23,220
30	Indiana	22,997
31	Maryland	22,779
32	California	22,528
33	Minnesota	22,456
34	Massachusetts	21,206
35	Kentucky	20,840
36	Virginia	19,772
37	New Mexico	18,463
38	Puerto Rico	18,420
39	Connecticut	18,195
40	Alaska	17,167
41	Wyoming	17,015
42	Michigan	16,807
43	Ohio	16,083
44	Colorado	15,497
45	Pennsylvania	14,946
46	Washington	13,739
47	West Virginia	11,569
48	Hawaii	10,155
49	Oregon	9,592
50	New Hampshire	7,293
51	Maine	4,483
52	Vermont	3,158

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	956
2	Wisconsin	948
3	South Dakota	668
4	Montana	622
5	Idaho	527
6	Wyoming	474
7	Rhode Island	450
8	Kansas	409
9	Nebraska	385
10	Utah	377
11	Tennessee	340
12	Iowa	328
13	New Mexico	306
14	Oklahoma	299
15	Illinois	296
16	Alaska	284
17	Michigan	284
18	Arkansas	279
19	Kentucky	266
20	Indiana	238
21	Minnesota	223
22	Mississippi	211
23	Missouri	209
24	Alabama	207
25	Colorado	199
26	Nevada	196
27	Texas	194
28	Connecticut	190
29	Ohio	177
30	Louisiana	162
31	Puerto Rico	160
32	North Carolina	155
33	South Carolina	153
34	Arizona	128
35	Massachusetts	121
36	West Virginia	121
37	New Jersey	120
38	Pennsylvania	119
39	Florida	116
40	Georgia	110
41	Virginia	102
42	California	99
43	Washington	97
44	Delaware	96
45	Maryland	86
46	Oregon	73
47	New York	72
48	District of Columbia	60
49	New Hampshire	54
50	Hawaii	46
51	Maine	21
52	Vermont	18

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,828
2	New York	1,696
3	Massachusetts	1,418
4	Connecticut	1,280
5	Louisiana	1,245
6	Rhode Island	1,103
7	Mississippi	1,082
8	District of Columbia	909
9	Arizona	804
10	Illinois	759
11	Florida	754
12	Michigan	742
13	South Carolina	720
14	Georgia	708
15	Delaware	688
16	Pennsylvania	674
17	Maryland	671
18	Texas	611
19	Indiana	597
20	Arkansas	580
21	Alabama	576
22	Nevada	563
23	North Dakota	560
24	Iowa	505
25	New Mexico	453
26	Ohio	440
27	Missouri	436
28	California	435
29	Tennessee	431
30	Minnesota	413
31	Virginia	411
32	North Carolina	387
33	Colorado	385
34	South Dakota	376
35	New Hampshire	344
36	Kansas	326
37	Kentucky	318
38	Washington	313
39	Idaho	308
40	Oklahoma	305
41	Nebraska	302
42	Wisconsin	293
43	Montana	258
44	Puerto Rico	242
45	West Virginia	230
46	Utah	174
47	Oregon	151
48	Hawaii	142
49	Maine	108
50	Wyoming	105
51	Vermont	92
52	Alaska	87

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kansas	10
2	Montana	10
3	North Dakota	7
4	Iowa	6
5	Wisconsin	6
6	Arkansas	5
7	Mississippi	5
8	Rhode Island	5
9	Indiana	4
10	Nebraska	4
11	Florida	3
12	Hawaii	3
13	Idaho	3
14	Illinois	3
15	Kentucky	3
16	Michigan	3
17	North Carolina	3
18	Oklahoma	3
19	South Carolina	3
20	South Dakota	3
21	Tennessee	3
22	Virginia	3
23	Alabama	2
24	Connecticut	2
25	Louisiana	2
26	Massachusetts	2
27	Minnesota	2
28	Nevada	2
29	New Mexico	2
30	Ohio	2
31	Texas	2
32	West Virginia	2
33	Wyoming	2
34	Arizona	1
35	California	1
36	Colorado	1
37	Delaware	1
38	Georgia	1
39	Maryland	1
40	Missouri	1
41	New Jersey	1
42	Oregon	1
43	Pennsylvania	1
44	Utah	1
45	Washington	1
46	Alaska	0
47	District of Columbia	0
48	Maine	0
49	New Hampshire	0
50	New York	0
51	Puerto Rico	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	170,762	1	99
Chattahoochee	Georgia	159,072	2	99
Trousdale	Tennessee	154,555	3	99
Lafayette	Florida	150,202	4	99
Lake	Tennessee	139,111	5	99
Davidson	Tennessee	46,547	260	91
Richland	South Carolina	40,398	403	87
York	South Carolina	22,624	1434	54
Orange	California	18,720	1772	43
Pierce	Washington	11,696	2447	22

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Emporia city	Virginia	5,238	1	99
Hancock	Georgia	5,203	2	99
Kenedy	Texas	4,950	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	637	809	74
Davidson	Tennessee	473	1147	63
Orange	California	448	1211	61
York	South Carolina	342	1506	52
Pierce	Washington	269	1739	44

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.